Lilly Tech Systems
FastAPI for AI
Serve machine learning models as production-ready REST APIs. Learn async endpoints, Pydantic validation, streaming LLM responses, WebSocket real-time inference, Docker deployment, and more.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Why FastAPI for ML model serving? Async architecture, auto-docs, Pydantic, and comparison with Flask.
2. Setup
Install FastAPI and Uvicorn, create your first endpoint, request/response models, and project structure.
3. Serving Models
Serve scikit-learn, PyTorch, and TensorFlow models. Model loading, prediction endpoints, and batch inference.
4. Streaming
Stream LLM responses with Server-Sent Events (SSE), WebSocket for real-time, and background tasks.
5. Authentication
API key auth, OAuth2, JWT tokens, rate limiting, and securing your ML API endpoints.
6. Best Practices
Docker deployment, health checks, logging, monitoring, testing, and production architecture patterns.
What You'll Learn
By the end of this course, you'll be able to:
Serve ML Models
Deploy scikit-learn, PyTorch, and TensorFlow models as high-performance REST APIs with automatic validation.
Stream LLM Output
Build streaming endpoints for LLM token-by-token output using SSE and WebSocket protocols.
Secure Your API
Implement authentication, rate limiting, and access control for production ML APIs.
Deploy with Docker
Containerize and deploy your FastAPI ML service with health checks, logging, and monitoring.